Noise Reduction in a Reputation Index
AbstractAssuming that a time series incorporates “signal” and “noise” components, we propose a method to estimate the extent of the “noise” component by considering the smoothing properties of the state-space of the time series. A mild degree of smoothing in the state-space, applied using a Kalman filter, allows for noise estimation arising from the measurement process. It is particularly suited in the context of a reputation index, because small amounts of noise can easily mask more significant effects. Adjusting the state-space noise measurement parameter leads to a limiting smoothing situation, from which the extent of noise can be estimated. The results indicate that noise constitutes approximately 10% of the raw signal: approximately 40 decibels. A comparison with low pass filter methods (Butterworth in particular) is made, although low pass filters are more suitable for assessing total signal noise. View Full-Text
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Mitic, P. Noise Reduction in a Reputation Index. Int. J. Financial Stud. 2018, 6, 19.
Mitic P. Noise Reduction in a Reputation Index. International Journal of Financial Studies. 2018; 6(1):19.Chicago/Turabian Style
Mitic, Peter. 2018. "Noise Reduction in a Reputation Index." Int. J. Financial Stud. 6, no. 1: 19.
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